Start by looking at quantity and mix of products sold in Pullman, comparing the school year and summertime.
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
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## Warning: Removed 1 rows containing non-finite values (stat_smooth).
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## Warning: Removed 1 rows containing missing values (geom_point).
##
## Call:
## lm(formula = perc.usable ~ college + beforeSummer + college_before,
## data = did_pullman_seattle)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.043052 -0.019305 0.002384 0.015831 0.049189
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.719350 0.009524 75.529 <2e-16 ***
## college 0.005346 0.013469 0.397 0.695
## beforeSummer -0.018644 0.013469 -1.384 0.179
## college_before 0.026223 0.019048 1.377 0.181
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0252 on 24 degrees of freedom
## Multiple R-squared: 0.1997, Adjusted R-squared: 0.09965
## F-statistic: 1.996 on 3 and 24 DF, p-value: 0.1414
##
## Call:
## lm(formula = tot_items ~ college + beforeSummer + college_before,
## data = did_pullman_seattle)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4452.1 -341.6 -64.6 259.6 5245.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 16589.1 909.3 18.244 1.42e-15 ***
## college -15836.7 1285.9 -12.316 7.29e-12 ***
## beforeSummer 3317.0 1285.9 2.580 0.0164 *
## college_before -3314.3 1818.5 -1.822 0.0809 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2406 on 24 degrees of freedom
## Multiple R-squared: 0.9401, Adjusted R-squared: 0.9326
## F-statistic: 125.6 on 3 and 24 DF, p-value: 8.321e-15
##
## Call:
## lm(formula = perc.extracts ~ college + beforeSummer + college_before,
## data = did_pullman_seattle)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0238026 -0.0033981 -0.0000447 0.0036002 0.0298770
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.111405 0.004454 25.014 < 2e-16 ***
## college -0.014757 0.006298 -2.343 0.027751 *
## beforeSummer 0.001647 0.006298 0.261 0.795940
## college_before 0.036798 0.008907 4.131 0.000378 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01178 on 24 degrees of freedom
## Multiple R-squared: 0.6129, Adjusted R-squared: 0.5645
## F-statistic: 12.67 on 3 and 24 DF, p-value: 3.659e-05
##
## Call:
## lm(formula = perc.edible ~ college + beforeSummer + college_before,
## data = did_pullman_seattle)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.033336 -0.015386 -0.002612 0.013170 0.033015
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.131097 0.007554 17.356 4.36e-15 ***
## college -0.018924 0.010682 -1.772 0.0892 .
## beforeSummer 0.011427 0.010682 1.070 0.2954
## college_before -0.039443 0.015107 -2.611 0.0153 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01998 on 24 degrees of freedom
## Multiple R-squared: 0.5876, Adjusted R-squared: 0.5361
## F-statistic: 11.4 on 3 and 24 DF, p-value: 7.669e-05
##
## Call:
## lm(formula = perc.usable ~ college + beforeSummer + college_before,
## data = did_pullman_spokane)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.043052 -0.012561 0.001369 0.013180 0.049189
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.716946 0.008565 83.705 <2e-16 ***
## college 0.015329 0.012113 1.266 0.218
## beforeSummer 0.009995 0.012113 0.825 0.417
## college_before -0.017574 0.017130 -1.026 0.315
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02266 on 24 degrees of freedom
## Multiple R-squared: 0.06454, Adjusted R-squared: -0.0524
## F-statistic: 0.5519 on 3 and 24 DF, p-value: 0.6518
##
## Call:
## lm(formula = tot_items ~ college + beforeSummer + college_before,
## data = did_pullman_spokane)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1158.43 -464.46 -91.29 114.61 1739.57
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7017.4 299.9 23.396 < 2e-16 ***
## college -6262.3 424.2 -14.763 1.53e-13 ***
## beforeSummer -992.9 424.2 -2.341 0.0279 *
## college_before 990.1 599.9 1.651 0.1119
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 793.6 on 24 degrees of freedom
## Multiple R-squared: 0.9399, Adjusted R-squared: 0.9324
## F-statistic: 125.1 on 3 and 24 DF, p-value: 8.738e-15
##
## Call:
## lm(formula = perc.extracts ~ college + beforeSummer + college_before,
## data = did_pullman_spokane)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.023803 -0.010274 -0.002200 0.008437 0.029877
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.168860 0.005192 32.521 < 2e-16 ***
## college -0.033767 0.007343 -4.598 0.000115 ***
## beforeSummer 0.003008 0.007343 0.410 0.685678
## college_before -0.041454 0.010385 -3.992 0.000538 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01374 on 24 degrees of freedom
## Multiple R-squared: 0.8516, Adjusted R-squared: 0.8331
## F-statistic: 45.91 on 3 and 24 DF, p-value: 4.27e-10
##
## Call:
## lm(formula = perc.edible ~ college + beforeSummer + college_before,
## data = did_pullman_spokane)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.033336 -0.005723 -0.000689 0.004758 0.028886
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.075373 0.005560 13.555 9.64e-13 ***
## college 0.008785 0.007864 1.117 0.27500
## beforeSummer -0.005670 0.007864 -0.721 0.47786
## college_before 0.033685 0.011121 3.029 0.00579 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01471 on 24 degrees of freedom
## Multiple R-squared: 0.5894, Adjusted R-squared: 0.5381
## F-statistic: 11.48 on 3 and 24 DF, p-value: 7.285e-05
## [1] "collegetown" "saledate" "revenue"
## [4] "tot_items" "avg_price_peritem" "perc.usable"
## [7] "perc.edible" "perc.extracts"
##
## Call:
## lm(formula = perc.usable ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.043052 -0.014397 0.001367 0.013006 0.049189
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.714375 0.008759 81.558 <2e-16 ***
## college 0.017900 0.012387 1.445 0.161
## beforeSummer 0.016998 0.012387 1.372 0.183
## college_before -0.024577 0.017518 -1.403 0.173
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02317 on 24 degrees of freedom
## Multiple R-squared: 0.1, Adjusted R-squared: -0.01246
## F-statistic: 0.8893 on 3 and 24 DF, p-value: 0.4608
##
## Call:
## lm(formula = perc.extracts ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0238026 -0.0039328 0.0001344 0.0046867 0.0298770
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1415556 0.0044484 31.821 < 2e-16 ***
## college -0.0064627 0.0062910 -1.027 0.314526
## beforeSummer -0.0007699 0.0062910 -0.122 0.903618
## college_before -0.0376756 0.0088969 -4.235 0.000291 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01177 on 24 degrees of freedom
## Multiple R-squared: 0.7439, Adjusted R-squared: 0.7119
## F-statistic: 23.24 on 3 and 24 DF, p-value: 2.804e-07
##
## Call:
## lm(formula = perc.edible ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.033336 -0.010212 -0.003450 0.008487 0.028886
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.107721 0.006312 17.067 6.33e-15 ***
## college -0.023563 0.008926 -2.640 0.01435 *
## beforeSummer -0.008578 0.008926 -0.961 0.34612
## college_before 0.036594 0.012623 2.899 0.00788 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0167 on 24 degrees of freedom
## Multiple R-squared: 0.3234, Adjusted R-squared: 0.2388
## F-statistic: 3.824 on 3 and 24 DF, p-value: 0.02268
##
## Call:
## lm(formula = perc.usable ~ college + fallSemester + college_fall,
## data = did_pullman_noncollege_fallsem)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.053011 -0.011930 0.001334 0.009271 0.048185
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.715931 0.008923 80.233 < 2e-16 ***
## college 0.046743 0.012619 3.704 0.00111 **
## fallSemester 0.003667 0.012619 0.291 0.77384
## college_fall -0.034699 0.017846 -1.944 0.06367 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02361 on 24 degrees of freedom
## Multiple R-squared: 0.4144, Adjusted R-squared: 0.3412
## F-statistic: 5.661 on 3 and 24 DF, p-value: 0.004439
##
## Call:
## lm(formula = perc.extracts ~ college + fallSemester + college_fall,
## data = did_pullman_noncollege_fallsem)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.021738 -0.005143 -0.000400 0.003117 0.032301
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.142761 0.004147 34.428 < 2e-16 ***
## college -0.020774 0.005864 -3.543 0.00166 **
## fallSemester 0.001342 0.005864 0.229 0.82089
## college_fall 0.023657 0.008293 2.853 0.00879 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01097 on 24 degrees of freedom
## Multiple R-squared: 0.488, Adjusted R-squared: 0.4241
## F-statistic: 7.626 on 3 and 24 DF, p-value: 0.0009481
##
## Call:
## lm(formula = perc.edible ~ college + fallSemester + college_fall,
## data = did_pullman_noncollege_fallsem)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.034076 -0.009247 -0.000059 0.010763 0.041184
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.097600 0.006544 14.914 1.23e-13 ***
## college -0.012304 0.009255 -1.329 0.196
## fallSemester -0.009509 0.009255 -1.027 0.314
## college_fall 0.019212 0.013088 1.468 0.155
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01731 on 24 degrees of freedom
## Multiple R-squared: 0.08831, Adjusted R-squared: -0.02565
## F-statistic: 0.7749 on 3 and 24 DF, p-value: 0.5194
##
## Call:
## lm(formula = perc.usable ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.043052 -0.013132 0.002439 0.012509 0.049189
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.742694 0.008523 87.139 <2e-16 ***
## college -0.010419 0.012053 -0.864 0.396
## beforeSummer 0.015568 0.012053 1.292 0.209
## college_before -0.023147 0.017046 -1.358 0.187
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02255 on 24 degrees of freedom
## Multiple R-squared: 0.2666, Adjusted R-squared: 0.1749
## F-statistic: 2.907 on 3 and 24 DF, p-value: 0.05533
##
## Call:
## lm(formula = perc.edible ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.033336 -0.008104 -0.003345 0.009915 0.028886
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.089731 0.005983 14.998 1.09e-13 ***
## college -0.005573 0.008461 -0.659 0.51636
## beforeSummer -0.007654 0.008461 -0.905 0.37465
## college_before 0.035670 0.011966 2.981 0.00649 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01583 on 24 degrees of freedom
## Multiple R-squared: 0.3997, Adjusted R-squared: 0.3247
## F-statistic: 5.327 on 3 and 24 DF, p-value: 0.005884
##
## Call:
## lm(formula = perc.extracts ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0238026 -0.0037265 -0.0008578 0.0052419 0.0298770
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.136883 0.004469 30.629 < 2e-16 ***
## college -0.001790 0.006320 -0.283 0.779407
## beforeSummer -0.002769 0.006320 -0.438 0.665222
## college_before -0.035677 0.008938 -3.991 0.000538 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01182 on 24 degrees of freedom
## Multiple R-squared: 0.7018, Adjusted R-squared: 0.6645
## F-statistic: 18.83 on 3 and 24 DF, p-value: 1.696e-06
##
## Call:
## lm(formula = avg_price_peritem ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.11647 -0.45267 0.08998 0.70174 1.59286
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.2816 0.3879 62.604 < 2e-16 ***
## college 3.7463 0.5485 6.830 4.6e-07 ***
## beforeSummer 0.3974 0.5485 0.724 0.476
## college_before -0.2763 0.7757 -0.356 0.725
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.026 on 24 degrees of freedom
## Multiple R-squared: 0.784, Adjusted R-squared: 0.757
## F-statistic: 29.04 on 3 and 24 DF, p-value: 3.718e-08
##
## Call:
## lm(formula = avg_price_peritem ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.11647 -0.45267 0.08998 0.70174 1.59286
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.2816 0.3879 62.604 < 2e-16 ***
## college 3.7463 0.5485 6.830 4.6e-07 ***
## beforeSummer 0.3974 0.5485 0.724 0.476
## college_before -0.2763 0.7757 -0.356 0.725
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.026 on 24 degrees of freedom
## Multiple R-squared: 0.784, Adjusted R-squared: 0.757
## F-statistic: 29.04 on 3 and 24 DF, p-value: 3.718e-08
##
## Call:
## lm(formula = tot_items ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6525.4 -1177.2 -104.4 114.6 8020.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28286 1311 21.578 < 2e-16 ***
## college -27531 1854 -14.850 1.35e-13 ***
## beforeSummer -3956 1854 -2.134 0.0433 *
## college_before 3953 2622 1.508 0.1447
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3468 on 24 degrees of freedom
## Multiple R-squared: 0.9413, Adjusted R-squared: 0.9339
## F-statistic: 128.2 on 3 and 24 DF, p-value: 6.617e-15
##
## Call:
## lm(formula = revenue ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -152413 -48210 -2171 5381 222191
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 686982 33927 20.249 < 2e-16 ***
## college -665779 47980 -13.876 5.84e-13 ***
## beforeSummer -85085 47980 -1.773 0.0889 .
## college_before 85112 67854 1.254 0.2218
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 89760 on 24 degrees of freedom
## Multiple R-squared: 0.9342, Adjusted R-squared: 0.9259
## F-statistic: 113.5 on 3 and 24 DF, p-value: 2.586e-14
##
## Call:
## lm(formula = perc.usable ~ college + fallSemester + college_fall,
## data = did_fallsem_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.053011 -0.008539 0.001830 0.007296 0.048185
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.720918 0.008684 83.018 < 2e-16 ***
## college 0.041756 0.012281 3.400 0.00236 **
## fallSemester 0.002770 0.012281 0.226 0.82345
## college_fall -0.033801 0.017368 -1.946 0.06343 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02298 on 24 degrees of freedom
## Multiple R-squared: 0.3787, Adjusted R-squared: 0.301
## F-statistic: 4.876 on 3 and 24 DF, p-value: 0.008697
##
## Call:
## lm(formula = perc.edible ~ college + fallSemester + college_fall,
## data = did_fallsem_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.034076 -0.009811 -0.000522 0.007139 0.041184
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.087482 0.006345 13.787 6.71e-13 ***
## college -0.002186 0.008974 -0.244 0.810
## fallSemester -0.007769 0.008974 -0.866 0.395
## college_fall 0.017472 0.012691 1.377 0.181
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01679 on 24 degrees of freedom
## Multiple R-squared: 0.1106, Adjusted R-squared: -0.0005933
## F-statistic: 0.9947 on 3 and 24 DF, p-value: 0.4122
##
## Call:
## lm(formula = perc.extracts ~ college + fallSemester + college_fall,
## data = did_fallsem_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.021738 -0.004491 -0.000549 0.003308 0.032301
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1507051 0.0041289 36.500 < 2e-16 ***
## college -0.0287187 0.0058392 -4.918 5.11e-05 ***
## fallSemester 0.0001914 0.0058392 0.033 0.97412
## college_fall 0.0248075 0.0082579 3.004 0.00615 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01092 on 24 degrees of freedom
## Multiple R-squared: 0.5858, Adjusted R-squared: 0.534
## F-statistic: 11.31 on 3 and 24 DF, p-value: 8.073e-05
##
## Call:
## lm(formula = avg_price_peritem ~ college + fallSemester + college_fall,
## data = did_fallsem_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1992 -0.4471 0.1483 0.4487 1.9141
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.7673 0.3971 62.371 < 2e-16 ***
## college -3.9935 0.5616 -7.111 2.38e-07 ***
## fallSemester -1.0184 0.5616 -1.814 0.0823 .
## college_fall 1.4477 0.7942 1.823 0.0808 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.051 on 24 degrees of freedom
## Multiple R-squared: 0.7491, Adjusted R-squared: 0.7178
## F-statistic: 23.89 on 3 and 24 DF, p-value: 2.194e-07
##
## Call:
## lm(formula = tot_items ~ college + fallSemester + college_fall,
## data = did_fallsem_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7111.0 -1652.5 -117.5 145.2 10587.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 39166 1491 26.276 < 2e-16 ***
## college -38061 2108 -18.056 1.8e-15 ***
## fallSemester 2523 2108 1.197 0.243
## college_fall -2170 2981 -0.728 0.474
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3944 on 24 degrees of freedom
## Multiple R-squared: 0.9664, Adjusted R-squared: 0.9622
## F-statistic: 230.4 on 3 and 24 DF, p-value: < 2.2e-16
##
## Call:
## lm(formula = revenue ~ college + fallSemester + college_fall,
## data = did_fallsem_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -168695 -43576 -3037 2588 260398
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 969082 36849 26.299 < 2e-16 ***
## college -946120 52112 -18.155 1.59e-15 ***
## fallSemester 22211 52112 0.426 0.674
## college_fall -14259 73698 -0.193 0.848
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 97490 on 24 degrees of freedom
## Multiple R-squared: 0.9654, Adjusted R-squared: 0.9611
## F-statistic: 223.1 on 3 and 24 DF, p-value: < 2.2e-16
##
## Call:
## lm(formula = perc.usable ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.043052 -0.013132 0.002439 0.012509 0.049189
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.742694 0.008523 87.139 <2e-16 ***
## college -0.010419 0.012053 -0.864 0.396
## beforeSummer 0.015568 0.012053 1.292 0.209
## college_before -0.023147 0.017046 -1.358 0.187
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02255 on 24 degrees of freedom
## Multiple R-squared: 0.2666, Adjusted R-squared: 0.1749
## F-statistic: 2.907 on 3 and 24 DF, p-value: 0.05533
##
## Call:
## lm(formula = perc.edible ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.033336 -0.008104 -0.003345 0.009915 0.028886
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.089731 0.005983 14.998 1.09e-13 ***
## college -0.005573 0.008461 -0.659 0.51636
## beforeSummer -0.007654 0.008461 -0.905 0.37465
## college_before 0.035670 0.011966 2.981 0.00649 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01583 on 24 degrees of freedom
## Multiple R-squared: 0.3997, Adjusted R-squared: 0.3247
## F-statistic: 5.327 on 3 and 24 DF, p-value: 0.005884
##
## Call:
## lm(formula = perc.extracts ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0238026 -0.0037265 -0.0008578 0.0052419 0.0298770
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.136883 0.004469 30.629 < 2e-16 ***
## college -0.001790 0.006320 -0.283 0.779407
## beforeSummer -0.002769 0.006320 -0.438 0.665222
## college_before -0.035677 0.008938 -3.991 0.000538 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01182 on 24 degrees of freedom
## Multiple R-squared: 0.7018, Adjusted R-squared: 0.6645
## F-statistic: 18.83 on 3 and 24 DF, p-value: 1.696e-06
##
## Call:
## lm(formula = avg_price_peritem ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.11647 -0.45267 0.08998 0.70174 1.59286
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.2816 0.3879 62.604 < 2e-16 ***
## college 3.7463 0.5485 6.830 4.6e-07 ***
## beforeSummer 0.3974 0.5485 0.724 0.476
## college_before -0.2763 0.7757 -0.356 0.725
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.026 on 24 degrees of freedom
## Multiple R-squared: 0.784, Adjusted R-squared: 0.757
## F-statistic: 29.04 on 3 and 24 DF, p-value: 3.718e-08
##
## Call:
## lm(formula = avg_price_peritem ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.11647 -0.45267 0.08998 0.70174 1.59286
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.2816 0.3879 62.604 < 2e-16 ***
## college 3.7463 0.5485 6.830 4.6e-07 ***
## beforeSummer 0.3974 0.5485 0.724 0.476
## college_before -0.2763 0.7757 -0.356 0.725
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.026 on 24 degrees of freedom
## Multiple R-squared: 0.784, Adjusted R-squared: 0.757
## F-statistic: 29.04 on 3 and 24 DF, p-value: 3.718e-08
##
## Call:
## lm(formula = tot_items ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6525.4 -1177.2 -104.4 114.6 8020.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28286 1311 21.578 < 2e-16 ***
## college -27531 1854 -14.850 1.35e-13 ***
## beforeSummer -3956 1854 -2.134 0.0433 *
## college_before 3953 2622 1.508 0.1447
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3468 on 24 degrees of freedom
## Multiple R-squared: 0.9413, Adjusted R-squared: 0.9339
## F-statistic: 128.2 on 3 and 24 DF, p-value: 6.617e-15
##
## Call:
## lm(formula = revenue ~ college + beforeSummer + college_before,
## data = did_pullman_noncollege_nonurban)
##
## Residuals:
## Min 1Q Median 3Q Max
## -152413 -48210 -2171 5381 222191
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 686982 33927 20.249 < 2e-16 ***
## college -665779 47980 -13.876 5.84e-13 ***
## beforeSummer -85085 47980 -1.773 0.0889 .
## college_before 85112 67854 1.254 0.2218
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 89760 on 24 degrees of freedom
## Multiple R-squared: 0.9342, Adjusted R-squared: 0.9259
## F-statistic: 113.5 on 3 and 24 DF, p-value: 2.586e-14